Stephen Wolfram is giddy with excitement. A long post, but worth a read all the way through.

I’m not sure what is coming, but whatever it is, I am starting to formulate an answer for that inevitable and challenging moment when my daughters ask me why they should bother to learn mathematics.

In fact, with (potentially) big steps forward like this laying a foundation now, maths teaching is going to be revolutionised and the above question will no longer be relevant. After learning the basics, advances of this kind will allow people to see the beauty of mathematics.

The young will grow up with things like this. In the same way they are all “digital natives” now, they will be the first generation to be “computation native” too.

They will grow up appreciating mathematics in a real-world context much more than most people of my (or any previous) generation.

…recently something amazing has happened. We’ve figured out how to take all these threads, and all the technology we’ve built, to create something at a whole different level. The power of what is emerging continues to surprise me. But already I think it’s clear that it’s going to be profoundly important in the technological world, and beyond.

At some level it’s a vast unified web of technology that builds on what we’ve created over the past quarter century. At some level it’s an intellectual structure that actualizes a new computational view of the world. And at some level it’s a practical system and framework that’s going to be a fount of incredibly useful new services and products.

It’s hard to foresee the ultimate consequences of what we’re doing. But the beginning is to provide a way to inject sophisticated computation and knowledge into everything—and to make it universally accessible to humans, programs and machines, in a way that lets all of them interact at a vastly richer and higher level than ever before.

A crucial building block of all this is what we’re calling the Wolfram Language.

We call it the Wolfram Language because it is a language. But it’s a new and different kind of language. It’s a general-purpose knowledge-based language. That covers all forms of computing, in a new way.

There are plenty of existing general-purpose computer languages. But their vision is very different—and in a sense much more modest—than the Wolfram Language.

And so in the Wolfram Language, built right into the language, are capabilities for laying out graphs or doing image processing or creating user interfaces or whatever. Inside there’s a giant web of algorithms—by far the largest ever assembled, and many invented by us. And there are then thousands of carefully designed functions set up to use these algorithms to perform operations as automatically as possible.

So in a sense inside the Wolfram Language we have a whole computable model of the world.

It can be an array of data. Or a piece of graphics. Or an algebraic formula. Or a network. Or a time series. Or a geographic location. Or a user interface. Or a document. Or a piece of code. All of these are just symbolic expressions which can be combined or manipulated in a very uniform way.

In most languages there’s a sharp distinction between programs, and data, and the output of programs. Not so in the Wolfram Language. It’s all completely fluid. Data becomes algorithmic. Algorithms become data. There’s no distinction needed between code and data. And everything becomes both intrinsically scriptable, and intrinsically interactive. And there’s both a new level of interoperability, and a new level of modularity.

And this is not just a theoretical idea. Thanks to endless layers of software engineering that we’ve done over the years—and lots of automation—it’s absolutely practical, and spectacular. The Wolfram Language can immediately describe its own deployment. Whether it’s creating an instant API, or putting up an interactive web page, or creating a mobile app, or collecting data from a network of embedded programs.

And what’s more, it can do it transparently across desktop, cloud, mobile, enterprise and embedded systems.

It’s been quite an amazing thing seeing this all start to work. And being able to create tiny programs that deploy computation across different systems in ways one had never imagined before.

There’ll be the Wolfram Data Science Platform, that allows one to connect to all sorts of data sources, then use the kind of automation seen in Wolfram|Alpha Pro, then pick out and modify Wolfram Language programs to do data science—and then use CDF to set up reports to generate automatically, on a schedule, through an API, or whatever.

And with our Wolfram Embedded Computation Platform, we’ll have the Wolfram Language running on all sorts of embedded systems, communicating with devices, as well as with the cloud and so on.

I’m very excited about all the things that are becoming possible. As the Wolfram Language gets deployed in all these different places, we’re increasingly going to be able to have a uniform symbolic representation for everything. Computation. Knowledge. Content. Interfaces. Infrastructure.

Just as the lines between data, content and code blur, so too will the lines between programming and mere input. Everything will become instantly programmable—by a very wide range of people, either by using the Wolfram Language directly, or by using free-form natural language.

Virtually any article today about big data inevitably turns to the notion that the country is suffering from a crucial shortage of data scientists.

What seems to be missing from all of these discussions, though, is a dialogue about how to steer around this bottleneck and make big data directly accessible to business leaders.

While difficult to generalize, there are three main roles served by the data scientist: data architecture, machine learning, and analytics.

The solution then lies in creating fit-to-purpose products and solutions that abstract away as much of the technical complexity as possible, so that the power of big data can be put into the hands of business users.

Data scientists are changing the way decisions happen by making better use of big data. Rather than finding ways around them, we need to make data science more accessible as a profession and need to provide easier tools for data scientists.

We build new systems that are flexible and dynamic and create more new jobs — such as data scientists — to analyze and build models for these new systems. It is obvious that in such a world, where static models cannot keep up, data scientists will be indispensable.

…data scientists are the designers and the content creators of today, not the software engineers or the IT bottleneck.

We need data scientists, and we need hundreds of thousands of them. They will do their magic, create new ways of experiencing life, products and services…

New, simpler tools will no doubt come along over time and it is something to look forward to.

I’m choosing to concentrate on the analytics angle of a Data Science role – know the right questions to ask, know how to state the questions so that you are delivered the answers you want to get and then be able to interpret the answers correctly, so that relevant decisions can be made. That is the ultimate goal.

This is the second part of James Ellroy’s “Underworld USA” trilogy that started with American Tabloid.

It begins in Dallas on 22 November 1963, just after the assassination of John F Kennedy, and literally hours after Tabloid ended. This is a continuation of the story in the truest sense of the word.

The Cold Six Thousand follows the paths of the main characters through to June 1968. This period was not the brightest in recent American history. It encompasses many elements: the JFK assassination, the Ku Klux Klan, the FBI’s attack on the civil rights movement, the start of the Vietnam war and the smuggling of heroin back to the US, the growth of Las Vegas and its take-over by Howard Hughes, and the murders of Martin Luther King and Robert Kennedy.

This, then, was never going to be an enjoyable tale. It is unflinching in its portrayal of bad acts by bad men – dark, unforgiving, unredeeming and unredeemed.

Ellroy puts his characters through so much that I wanted to see where he would take them next. I wasn’t quite ready for what I got.

In Tabloid, there was an attraction to the characters. There was something novel about the fictional characters being inserted into an exciting world of power, opulence, influence and movie stars. There has always been an infatuation with the Kennedys, and as much as you learned of the bad points, you still enjoyed going along for the ride.

Not so with TCST. The characters are weary from all that has gone before, the schemes get more intricate, the circles are closing in. Their way of life is taking its toll – how much more can they take?

It’s not comfortable reading, but it draws you in. You aren’t fascinated anymore – you are repelled. All the characters are deeply flawed. You don’t agree with their viewpoints or their actions – but still you read on.

One thing more than any other has divided readers over the quality of the book – the use of short sentences, usually only four or five words long.

This is a distinct break from the style of American Tabloid, and is like nothing you have read before. It should be easy to read – and it works when things heat up – but it makes the book very difficult to get into. And to sustain this over nearly 700 pages demands a lot of the reader. Not only are you repelled by what you are reading, but you have to put in some serious effort.

Am I recommending this book? Yes.

Are there any qualifications? Definitely read American Tabloid first, otherwise you will be lost. I read Tabloid four years ago, and in the end I had to reread it before starting out on TCST properly (not that that was any hardship, as it is one of the best books I’ve ever read.)

Then sit back, put your feet up, and squirm.

———
“The Cold Six Thousand is an exercise in audacity: a difficult book, which demands to be read on its own terms… This book is designed to shock, and shock it does.” – Financial Times
“The Cold Six Thousand is a huge canvas – it is the Sistine Chapel of American bad juju” – James Ellroy.
“And as always, in the bluntest and politest possible way, I will state that any critics that don’t like my book can kiss my fucking ass.” – James Ellroy.

After seeing L.A. Confidential I was so impressed by the strong storyline that I went straight to a bookshop to buy something else by the author. In Confidential I really enjoyed the way that the storyline assumes that you have a brain, but at the same time you never get too lost in details. Tabloid didn’t let me down either – I’ve read a few books on the JFK assassination, so it was good to be able to recognise some of the names as they popped up (quite a few of the names, in fact).

More than that is the way that the book is totally convincing – although it is fiction, things could well have happened like that. Maybe JFK’s womanizing is a bit overdone – I’ll never know.

The book left me feeling half way through as if I should be reaching the end of any normal book, if that makes any sense. It just seemed to pack in twice as much story as a lot of these thrillers that I read. The fact that it is based around real people and real events just serve to make it more poignant and stinging. Yes it’s violent; yes, there’s profanity, but it just serves to define the time in which it was set. It just seems to be more real than the kind of fairytale view we have grown up with.

Apparently it was Time’s novel of the year (1995, I think); in that case, it is well deserved, and if L.A. Confidential gets more people reading Ellroy, then so much the better.

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Anil Dash in discussion on Leo Laporte’s Triangulation programme. He talks about blogging, the US government’s social media policies and how to go about lobbying government so that it can better understand and promote the internet / web, rather than attack it. We could do with some of that in the UK too.